AI-Driven Data Anonymization Techniques in Health Care

AI-Driven Data Anonymization Techniques

Data privacy is a leading concern in many industries, but health care deals with more pressure than most. Patient information faces stringent regulations and can do significant damage if it leaks. At the same time, you need to be able to share and analyze it to improve care.

Healthcare data breaches average $10.93 million, more expensive than in any other industry. Thankfully, improvement is possible. Artificial intelligence (AI)-enabled anonymization techniques can hide sensitive details so these breaches don’t impact patient privacy. Here are five of these methods you’ll see in health care today.

1. Pseudonymization

The most basic of these anonymization techniques is pseudonymization. As the name suggests, this practice replaces personally identifiable information (PII) with fake details that serve the same purpose.

Replacing a patient’s name with “John Doe” won’t change any health details, so it’s useful but won’t reveal who it’s talking about if breached. The downside is that someone could theoretically reidentify the record with enough work. For that reason, the Health Insurance Portability and Accountability Act (HIPAA) says pseudonyms cannot come from related information about the patient — they must be entirely random.

2. Tokenization

“Tokenization has saved $650 million in fraud in the finance industry in 2023, so it has huge potential in the health care sector, too.” 

Tokenization is a similar but more complex method. Here, an AI algorithm generates a unique placeholder for PII in a health record. Like pseudonymization, the data is still usable for treatment and analysis without sacrificing privacy. However, it’s different in that it uses cryptography to generate these stand-ins, reducing the likelihood of reidentification.

Many tokens are temporary, so they change between functions to offer even more privacy. This practice has saved $650 million in fraud in the finance industry in 2023, so it has huge potential in the healthcare sector, too.

3. K-Anonymity

A less common but equally beneficial approach is to use K-anonymity. This method applies various masking techniques to keep the overall value of a dataset the same while changing the exact identifiers. For example, it could change all the names and addresses of a hospital’s patients but still contain the same levels of each demographic.

Because K-anonymity applies to entire datasets instead of individual records, you can’t use it for individualized applications. However, it’s still useful for medical research, such as tracing health trends across a population.

4. Dynamic Data Masking

Sometimes, the amount of PII you should remove depends on the situation. Dynamic data masking (DDM) serves this need by changing how much information it hides depending on the context. That could mean removing more details when users with less authorization access it or removing more PII for machine learning applications than patient care cases.

HIPAA requires role-based access controls, and DDM makes it easier to implement these restrictions. Using AI to determine who can access what data simplifies this decision-making, enabling faster care while preserving privacy. 

“Sometimes, how much PII you should remove changes depending on the situation.” 

5. Synthetic Data

Synthetic data is unique in that it eliminates all PII from a database. Here, machine learning models generate entirely original information that mimics the real world. The resulting dataset looks and behaves like patient data but contains no basis in reality.

This method is the most secure option, as it has no ties to actual patients’ information. However, for that very reason, it’s also of limited use in health care. You can train AI models with it, but it won’t work for research or patient care applications.

Choosing a Data Anonymization Method

“Health care organizations shouldn’t rely on a single data anonymization method. Instead, you should employ various techniques depending on the specific use case.” 

The best anonymization technique depends on your needs and goals. You can determine the optimal method by first reviewing any applicable regulations. That includes more than just HIPAA. For example, the International Medical Device Regulators Forum is a voluntary code, but partner organizations may look for it for assurance of higher standards.

These regulations may specify which methods are applicable for different types of data or situations. For example, you may be able to use pseudonymization for low-sensitivity tasks but must use tokenization or DDM for those involving more data sharing or vulnerabilities.

Similarly, you should consider your end goal. Masking techniques like pseudonymization and tokenization are not as secure but enable personalized health care. Synthetic data isn’t useful in that area, but it improves machine learning accuracy and maximizes security.

Given these complex considerations, healthcare organizations shouldn’t rely on a single data anonymization method. Instead, you should employ various techniques depending on the specific use case. Matching each application to the anonymization method that fits it best will produce the optimal balance between security and usability.

Health Care Data Needs Extensive Protection

Healthcare data is more sensitive — and, as a result, a bigger target for cybercrime — than any other form of information. In light of that risk, the medical industry must embrace privacy wherever it can. Anonymization is an important part of that goal.

These five methods are not the only ways to anonymize data. However, they are some of the most popular and effective strategies. Learning how each can benefit your workflows is key to protecting patient privacy while leveraging new technology.

Also, Read How Is AI Transforming the Landscape of Medical Technology?

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